Classifying and diagnosing systemic lupus erythematosus in the 21st century
Bibliographic record
Abstract
The EULAR/ACR 2019 classification criteria for SLE constitute a current and optimized clinical approach to SLE classification. Classification is still not based on molecular approaches and the results from large studies using polyomics may be interpreted as demonstrating the relevance of the genetic and environmental background rather than splitting SLE into several entities. In fact, an association study within the EULAR/ACR classification criteria project found associations between manifestations only within organ domains. This independency of various organ manifestations argues for SLE as one disease entity. The current review article will therefore concentrate on the clinical and immunological manifestations of SLE and on what we have already learned in this century. Moreover, the structure and essential rules of the EULAR/ACR 2019 classification criteria will be discussed. While classification and diagnosis are distinct concepts, which have to remain clearly separated, information derived from the process towards the classification criteria is also useful for diagnostic purposes. Therefore this article also tries to delineate what classification can teach us for diagnosis, covering a wide variety of SLE manifestations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".